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A simple graph database in SQLite

Project description


This is the PyPI package of the simple-graph implementation in Python, which is a simple graph database in SQLite, inspired by "SQLite as a document database".

Build and Test

How to generate the distribution archive and confirm it on, also based on the pypa/sampleproject:

rm -rf build dist src/simple_graph_sqlite.egg-info
python -m build
python -m twine upload --repository testpypi dist/*

Create a virtual environment for the test package, activate it, pull from (the --extra-index-url is necessary since the graphviz and/or Jinja2 dependencies may not be in the test archive), and confirm the package is available:

$ pip install -i --extra-index-url simple-graph-sqlite graphviz==0.16 Jinja2==3.1.2
$ python
Python 3.6.13 |Anaconda, Inc.| (default, Jun  4 2021, 14:25:59) 
[GCC 7.5.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from simple_graph_sqlite import database as db

With the test package installed, update PYTHONPATH to include ./tests and run pytest from the root of this repository. If the tests pass, rebuild and push to

rm -rf build dist src/simple_graph_sqlite.egg-info
python -m build
python -m twine upload --repository pypi dist/*


The schema consists of just two structures:

  • Nodes - these are any json objects, with the only constraint being that they each contain a unique id value
  • Edges - these are pairs of node id values, specifying the direction, with an optional json object as connection properties

There are also traversal functions as native SQLite Common Table Expressions:



Installation Requirements

Basic Functions

The database script provides convenience functions for atomic transactions to add, delete, connect, and search for nodes.

Any single node or path of nodes can also be depicted graphically by using the visualize function within the database script to generate dot files, which in turn can be converted to images with Graphviz.


Dropping into a python shell, we can create, upsert, and connect people from the early days of Apple Computer. The resulting database will be saved to a SQLite file named apple.sqlite:

>>> apple = "apple.sqlite"
>>> from simple_graph_sqlite import database as db
>>> db.initialize(apple)
>>> db.atomic(apple, db.add_node({'name': 'Apple Computer Company', 'type':['company', 'start-up'], 'founded': 'April 1, 1976'}, 1))
>>> db.atomic(apple, db.add_node({'name': 'Steve Wozniak', 'type':['person','engineer','founder']}, 2))
>>> db.atomic(apple, db.add_node({'name': 'Steve Jobs', 'type':['person','designer','founder']}, 3))
>>> db.atomic(apple, db.add_node({'name': 'Ronald Wayne', 'type':['person','administrator','founder']}, 4))
>>> db.atomic(apple, db.add_node({'name': 'Mike Markkula', 'type':['person','investor']}, 5))
>>> db.atomic(apple, db.connect_nodes(2, 1, {'action': 'founded'}))
>>> db.atomic(apple, db.connect_nodes(3, 1, {'action': 'founded'}))
>>> db.atomic(apple, db.connect_nodes(4, 1, {'action': 'founded'}))
>>> db.atomic(apple, db.connect_nodes(5, 1, {'action': 'invested', 'equity': 80000, 'debt': 170000}))
>>> db.atomic(apple, db.connect_nodes(1, 4, {'action': 'divested', 'amount': 800, 'date': 'April 12, 1976'}))
>>> db.atomic(apple, db.connect_nodes(2, 3))
>>> db.atomic(apple, db.upsert_node(2, {'nickname': 'Woz'}))

There are also bulk operations, to insert and connect lists of nodes in one transaction.

The nodes can be searched by their ids:

>>> db.atomic(apple, db.find_node(1))
{'name': 'Apple Computer Company', 'type': ['company', 'start-up'], 'founded': 'April 1, 1976', 'id': 1}

Searches can also use combinations of other attributes, both as strict equality, or using LIKE in combination with a trailing % for "starts with" or % at both ends for "contains":

>>> db.atomic(apple, db.find_nodes([db._generate_clause('name', predicate='LIKE')], ('Steve%',)))
[{'name': 'Steve Wozniak', 'type': ['person', 'engineer', 'founder'], 'id': 2, 'nickname': 'Woz'}, {'name': 'Steve Jobs', 'type': ['person', 'designer', 'founder'], 'id': 3}]
>>> db.atomic(apple, db.find_nodes([db._generate_clause('name', predicate='LIKE'), db._generate_clause('name', predicate='LIKE', joiner='OR')], ('%Woz%', '%Markkula',)))
[{'name': 'Steve Wozniak', 'type': ['person', 'engineer', 'founder'], 'id': 2, 'nickname': 'Woz'}, {'name': 'Mike Markkula', 'type': ['person', 'investor'], 'id': 5}]

More complex queries to introspect the json body, using the sqlite json_tree() function, are also possible, such as this query for every node whose type array contains the value founder:

>>> db.atomic(apple, db.find_nodes([db._generate_clause('type', tree=True)], ('founder',), tree_query=True, key='type'))
[{'name': 'Steve Wozniak', 'type': ['person', 'engineer', 'founder'], 'id': 2, 'nickname': 'Woz'}, {'name': 'Steve Jobs', 'type': ['person', 'designer', 'founder'], 'id': 3}, {'name': 'Ronald Wayne', 'type': ['person', 'administrator', 'founder'], 'id': 4}]

See the _generate_clause() and _generate_query() functions in for usage hints.

Paths through the graph can be discovered with a starting node id, and an optional ending id; the default neighbor expansion is nodes connected nodes in either direction, but that can changed by specifying either find_outbound_neighbors or find_inbound_neighbors instead:

>>> db.traverse(apple, 2, 3)
['2', '1', '3']
>>> db.traverse(apple, 4, 5)
['4', '1', '2', '3', '5']
>>> db.traverse(apple, 5, neighbors_fn=db.find_inbound_neighbors)
>>> db.traverse(apple, 5, neighbors_fn=db.find_outbound_neighbors)
['5', '1', '4']
>>> db.traverse(apple, 5, neighbors_fn=db.find_neighbors)
['5', '1', '2', '3', '4']

Any path or list of nodes can rendered graphically by using the visualize function. This command produces dot files, which are also rendered as images with Graphviz:

>>> from visualizers import graphviz_visualize
>>> graphviz_visualize(apple, '', [4, 1, 5])

The resulting text file also comes with an associated image (the default is png, but that can be changed by supplying a different value to the format parameter)

The default options include every key/value pair (excluding the id) in the node and edge objects, and there are display options to help refine what is produced:

>>> graphviz_visualize(apple, '', [4, 1, 5], exclude_node_keys=['type'], hide_edge_key=True)

The resulting dot file can be edited further as needed; the dot guide has more options and examples.

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